Learning machine training based on plan types

ABSTRACT

A system for training a learning machine accesses a training database of reference metadata that describes reference plans that include reference first-type plans and reference second-type plans. Such plans may be travel plans or other plans. The system trains the learning machine to distinguish candidate first-type plans from candidate second-type plans. The training of the learning machine is based on a set of decision trees generated from randomly selected subsets of the reference metadata, and the randomly selected subsets each describe a corresponding randomly selected portion of the reference plans. The system then modifies the trained learning machine based on asymmetrical penalties for incorrectly distinguishing candidate first-type plans from candidate second-type plans. The system then provides the modified learning machine for run-time use in classifying plans.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to the technicalfield of special-purpose machines that facilitate artificialintelligence, including software-configured computerized variants ofsuch special-purpose machines and improvements to such variants, and tothe technologies by which such special-purpose machines become improvedcompared to other special-purpose machines that facilitate artificialintelligence. Specifically, the present disclosure addresses systems andmethods to facilitate training a learning machine.

BACKGROUND

A machine in the form of a computer system may be configured (e.g., viasuitable software programming) to function as a learning machine. Forexample, the learning machine may be caused to undergo or otherwiseimplement a supervised learning algorithm by which the learning machineaccesses training data that specifies certain inputs that correspond tocertain (e.g., desired) outputs. The learning machine iterativelymodifies a function (e.g., an objective function) to optimize thefunction's ability to reproduce the training data's outputs from theircorresponding inputs. Another machine (e.g., another computer system)may be configured to train the learning machine, for example, byprogramming, commanding, or otherwise causing the learning machine toexecute the supervised learning algorithm, including its constituentoperations, resulting in the learning machine becoming trained based onthe training data.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings.

FIG. 1 is a network diagram illustrating a network environment suitablefor training a learning machine, according to some example embodiments.

FIG. 2 is a block diagram illustrating components of a trainer machinesuitable for training the learning machine, according to some exampleembodiments.

FIGS. 3 and 4 are flowcharts illustrating operations of the trainermachine in performing a method of training, modifying, and providing thelearning machine, according to some example embodiments.

FIG. 5 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium and perform any one or more of the methodologiesdiscussed herein.

SUMMARY

In accordance with the systems, methods, and machine-readable mediadescribed herein, a system (e.g., including a trainer machine or othercomputer system for training a learning machine) accesses a trainingdatabase of reference metadata descriptive of reference travel plansthat include reference first-type plans and reference second-type plans.The system then trains a learning machine to distinguish candidatefirst-type plans from candidate second-type plans. The training of thelearning machine is based on a set of decision trees generated fromrandomly selected subsets of the reference metadata, and the randomlyselected subsets each describe a corresponding randomly selected portionof the reference plans. The decision trees may include random decisiontrees, such as those generated by a Random Forests® technique. Thesystem then modifies the trained learning machine, and this modificationof the learning machine is based on asymmetrical penalties forincorrectly distinguishing candidate first-type plans from candidatesecond-type plans. The system then provides the modified learningmachine for run-time use (e.g., in classifying plans of the first typefrom plans of the second type). At this point, the modified, trainedlearning machine has been trained to distinguish candidate first-typeplans from candidate second-type plans based on the asymmetricalpenalties for incorrectly distinguishing candidate first-type plans fromcandidate second-type plans. According to various example embodiments,such reference or candidate plans may include reference or candidatetravel plans (e.g., travel itineraries, each including one or moreflights reserved or taken, one or more hotel stays reserved orcompleted, one or more car rentals reserved or completed, etc., or anysuitable combinations thereof).

DETAILED DESCRIPTION

Example methods (e.g., algorithms) facilitate training a learningmachine based on plan types, and example systems (e.g., special-purposemachines configured by special-purpose software) are configured tofacilitate training a learning machine based on plan types. Examplesmerely typify possible variations. Unless explicitly stated otherwise,structures (e.g., structural components, such as modules) are optionaland may be combined or subdivided, and operations (e.g., in a procedure,algorithm, or other function) may vary in sequence or be combined orsubdivided. In the following description, for purposes of explanation,numerous specific details are set forth to provide a thoroughunderstanding of various example embodiments. It will be evident to oneskilled in the art, however, that the present subject matter may bepracticed without these specific details.

For brevity and clarity, several example embodiments of the systems andmethods described herein discuss example scenarios in which the plansare travel plans (e.g., travel itineraries). However, other types ofplans are contemplated by the present subject matter (e.g., deliveryroutes, equipment maintenance plans, event schedules, or taskperformance sequences).

FIG. 1 is a network diagram illustrating a network environment 100suitable for training a learning machine 120, according to some exampleembodiments. The network environment 100 includes a trainer machine 110,a database 115, the learning machine 120, and devices 130 and 150, allcommunicatively coupled to each other via a network 190. The trainermachine 110, with or without the database 115, may form all or part of acloud 118 (e.g., a geographically distributed set of multiple machinesconfigured to function as a single server), which may form all or partof a network-based system 105 (e.g., a cloud-based server systemconfigured to provide one or more network-based services to the devices130 and 150). The trainer machine 110, the database 115, the learningmachine 120, and the devices 130 and 150 may each be implemented in aspecial-purpose (e.g., specialized) computer system, in whole or inpart, as described below with respect to FIG. 5 .

Also shown in FIG. 1 are users 132 and 152. One or both of the users 132and 152 may be a human user (e.g., a human being), a machine user (e.g.,a computer configured by a software program to interact with the device130 or 150), or any suitable combination thereof (e.g., a human assistedby a machine or a machine supervised by a human). The user 132 isassociated with the device 130 and may be a user of the device 130. Forexample, the device 130 may be a desktop computer, a vehicle computer, ahome media system (e.g., a home theater system or other homeentertainment system), a tablet computer, a navigational device, aportable media device, a smart phone, or a wearable device (e.g., asmart watch, smart glasses, smart clothing, or smart jewelry) belongingto the user 132. Likewise, the user 152 is associated with the device150 and may be a user of the device 150. As an example, the device 150may be a desktop computer, a vehicle computer, a home media system(e.g., a home theater system or other home entertainment system), atablet computer, a navigational device, a portable media device, a smartphone, or a wearable device (e.g., a smart watch, smart glasses, smartclothing, or smart jewelry) belonging to the user 152.

Any of the systems or machines (e.g., databases and devices) shown inFIG. 1 may be, include, or otherwise be implemented in a special-purpose(e.g., specialized or otherwise non-conventional and non-generic)computer that has been modified to perform one or more of the functionsdescribed herein for that system or machine (e.g., configured orprogrammed by special-purpose software, such as one or more softwaremodules of a special-purpose application, operating system, firmware,middleware, or other software program). For example, a special-purposecomputer system able to implement any one or more of the methodologiesdescribed herein is discussed below with respect to FIG. 5 , and such aspecial-purpose computer may accordingly be a means for performing anyone or more of the methodologies discussed herein. Within the technicalfield of such special-purpose computers, a special-purpose computer thathas been specially modified (e.g., configured by special-purposesoftware) by the structures discussed herein to perform the functionsdiscussed herein is technically improved compared to otherspecial-purpose computers that lack the structures discussed herein orare otherwise unable to perform the functions discussed herein.Accordingly, a special-purpose machine configured according to thesystems and methods discussed herein provides an improvement to thetechnology of similar special-purpose machines.

As used herein, a “database” is a data storage resource and may storedata structured as a text file, a table, a spreadsheet, a relationaldatabase (e.g., an object-relational database), a triple store, ahierarchical data store, or any suitable combination thereof. Moreover,any two or more of the systems or machines illustrated in FIG. 1 may becombined into a single system or machine, and the functions describedherein for any single system or machine may be subdivided among multiplesystems or machines.

The network 190 is a network that enables communication between or amongsystems, machines, databases, and devices (e.g., between the machine 110and the device 130). Accordingly, the network 190 may be a wirednetwork, a wireless network (e.g., a mobile or cellular network), or anysuitable combination thereof. The network 190 may include one or moreportions that constitute a private network, a public network (e.g., theInternet), or any suitable combination thereof. Accordingly, the network190 may include one or more portions that incorporate a local areanetwork (LAN), a wide area network (WAN), the Internet, a mobiletelephone network (e.g., a cellular network), a wired telephone network(e.g., a plain old telephone service (POTS) network), a wireless datanetwork (e.g., a WiFi network or WiMax network), or any suitablecombination thereof. Any one or more portions of the network 190 maycommunicate information via a transmission medium. As used herein,“transmission medium” refers to any intangible (e.g., transitory) mediumthat is capable of communicating (e.g., transmitting) instructions forexecution by a machine (e.g., by one or more processors of such amachine), and includes digital or analog communication signals or otherintangible media to facilitate communication of such software.

FIG. 2 is a block diagram illustrating components of the trainer machine110, as configured for training the learning machine 120, according tosome example embodiments. The trainer machine 110 is shown as includinga reference metadata accessor module 210, a learning machine trainermodule 220, a learning machine modifier module 230, and a learningmachine provider module 240, all configured to communicate with eachother (e.g., via a bus, shared memory, or a switch). The referencemetadata accessor module 210 may be or include custom software, customhardware, or both, configured to access reference metadata of referenceplans (e.g., stored by the database 115 and accessed therefrom). Thelearning machine trainer module 220 may be or include custom software,custom hardware, or both, configured to train the learning machine 120(e.g., initially train the learning machine 120 based on training data).The learning machine modifier module 230 may be or include customsoftware, custom hardware, or both, configured to modify the trainedlearning machine 120 (e.g., as trained by the learning machine trainermodule 220). The learning machine provider module 240 may be or includecustom software, custom hardware, or both, configured to provide (e.g.,for run-time usage) the modified, trained learning machine 120 (e.g., asmodified by the learning machine modifier module 230).

As shown in FIG. 2 , the reference metadata accessor module 210, thelearning machine trainer module 220, the learning machine modifiermodule 230, the learning machine provider module 240, or any suitablecombination thereof, may form all or part of an app 200 (e.g., a mobileapp) that is stored (e.g., installed) on the trainer machine 110 (e.g.,responsive to or otherwise as a result of data being received via thenetwork 190). Furthermore, one or more processors 299 (e.g., hardwareprocessors, digital processors, or any suitable combination thereof) maybe included (e.g., temporarily or permanently) in the app 200, thereference metadata accessor module 210, the learning machine trainermodule 220, the learning machine modifier module 230, the learningmachine provider module 240, or any suitable combination thereof.

Any one or more of the components (e.g., modules) described herein maybe implemented using hardware alone (e.g., one or more of the processors299) or a combination of hardware and software. For example, anycomponent described herein may physically include an arrangement of oneor more of the processors 299 (e.g., a subset of or among the processors299) configured to perform the operations described herein for thatcomponent. As another example, any component described herein mayinclude software, hardware, or both, that configure an arrangement ofone or more of the processors 299 to perform the operations describedherein for that component. Accordingly, different components describedherein may include and configure different arrangements of theprocessors 299 at different points in time or a single arrangement ofthe processors 299 at different points in time. Each component (e.g.,module) described herein is an example of a means for performing theoperations described herein for that component. Moreover, any two ormore components described herein may be combined into a singlecomponent, and the functions described herein for a single component maybe subdivided among multiple components. Furthermore, according tovarious example embodiments, components described herein as beingimplemented within a single system or machine (e.g., a single device)may be distributed across multiple systems or machines (e.g., multipledevices).

FIGS. 3 and 4 are flowcharts illustrating operations of the trainermachine 110 in performing a method 300 of training, modifying, andproviding the learning machine 120 for run-time use, according to someexample embodiments. Operations in the method 300 may be performed bythe trainer machine 110, using components (e.g., modules) describedabove with respect to FIG. 2 , using one or more processors (e.g.,microprocessors or other hardware processors), or using any suitablecombination thereof. As shown in FIG. 3 , the method 300 includesoperations 310, 320, 330, and 340.

In operation 310, the reference metadata accessor module 210 accesses atraining database (e.g., stored in the database 115) of referencemetadata. The accessed reference metadata corresponds to reference plans(e.g., a set of reference travel plans or other reference plans),describes aspects of the reference plans, and is associated (e.g., bythe training database) with these reference plans. Each one of thereference plans has its respectively corresponding associated referencemetadata that describes that reference plan. For example, the referenceplans may be reference travel plans that include reference first-typetravel plans and reference second-type travel plans. In various exampleembodiments, the first-type travel plans are travel plans classified aspersonal travel plans (e.g., for recreation or for family visits),non-tracked travel plans (e.g., for tax calculations or for otheraccounting calculations), non-reimbursable travel plans, non-businesstravel plans, or any suitable combination thereof; and the second-typetravel plans are travel plans classified as non-personal travel plans(e.g., for work or for non-recreational travel), tracked travel plans(e.g., for tax calculations or for other accounting calculations),reimbursable travel plans, business travel plans, or any suitablecombination thereof.

In operation 320, the learning machine trainer module 220 trains thelearning machine 120 (e.g., from an untrained state to a trained state,or from a pre-training state to a post-training state) to function as anartificially intelligent classifier configured to distinguish betweenfirst and second types of plans. Accordingly, by virtue of the learningmachine trainer module 220 performing operation 320, the learningmachine 120 becomes trained to distinguish between first and secondtypes of candidate plans (e.g., candidate travel itineraries to beclassified as first-type travel itineraries or second-type travelitineraries). That is, the trained learning machine 120 is configured bythis training process to determine whether a given candidate plan isclassified (e.g., categorized or labelled) as a first-type candidateplan or as a second-type candidate plan.

According to various example embodiments, the training of the learningmachine 120 in operation 320 may be based on one or more factors.Examples of such factors include: whether a plan is a one-way plan or around-trip plan (e.g., whether a flight in the plan was round-trip);whether a plan included international travel (e.g., whether the planincluded an international flight); the number of travelers correspondingto a plan (e.g., travelling together in the same travel itinerary); theday of the week on which a plan begins (e.g., Sunday, Monday, etc.); theday of the week on which a plan ends; the local time at which a planbegins; the local time at which a plan ends; the number of destinationsin a plan (e.g., the total count of destination cities or airports); thenumber of stops (e.g., layovers or stopovers) per destination in a plan(e.g., the ratio of stops to destination cities or airports); the sizesof the destination cities or airports in a plan (e.g., above or below apredetermined threshold size); whether the date or date range of a planincludes a holiday (e.g., includes a governmental holiday, such as afederal holiday); whether a plan includes a car rental reservation;whether a plan includes a hotel reservation; the source entity (e.g., atravel website among multiple travel websites) through which a plan wasreserved; and any suitable combination thereof.

Accordingly, a one-way plan may be more likely to be a first-type (e.g.,personal or recreational) plan, while a round-trip plan may be lesslikely to be a first-type plan, more likely to be a second-type (e.g.,non-personal or non-recreational) plan, or both. A plan that does notinclude international travel may be more likely to be a first-type plan,while a plan that includes international travel may be less likely to bea first-type plan, more likely to be a second-type plan, or both. A planwith a traveler count above a threshold value may be more likely to be afirst-type plan, while a plan with a traveler count at or below thethreshold value may be less likely to be a first-type plan, more likelyto be a second-type plan, or both. A plan that begins or ends on acertain day of the week may be more likely to be a first-type plan,while plans that do not may be less likely to be a first-type plan, morelikely to be a second-type plan, or both. A plan that begins or endsduring certain hours of the day in local time may be more likely to be afirst-type plan, while plans that do not may be less likely to be afirst-type plan, more likely to be a second-type plan, or both. A planwith a low number of destinations at or below a threshold value may bemore likely to be a first-type plan, while plans with high numbers ofdestinations above the threshold value may be less likely to be afirst-type plan, more likely to be a second-type plan, or both. A planwith a low number of stops (e.g., at or below a threshold value of zeroor one) may be more likely to be a first-type plan, while plans withhigh numbers of stops (e.g., above the threshold value) may be lesslikely to be a first-type plan, more likely to be a second-type plan, orboth. A plan with a low ratio of stops to destinations (e.g., at orbelow a threshold value) may be more likely to be a first-type plan,while plans with high ratios of stops to destinations (e.g., above thethreshold value) may be less likely to be a first-type plan, more likelyto be a second-type plan, or both.

Furthermore, a plan with destination cities or airports at or below athreshold size may be more likely to be a first-type plan, while planswith destination cities or airports above the threshold size may be lesslikely to be a first-type plan, more likely to be a second-type plan, orboth. A plan whose date or date range includes a holiday (e.g., agovernmental holiday) may be more likely to be a first-type plan, whileplans whose dates or date ranges do not may be less likely to be afirst-type plan, more likely to be a second-type plan, or both. A planthat includes a car rental reservation may be more likely to be afirst-type plan, while plans that do not may be less likely to be afirst-type plan, more likely to be a second-type plan, or both. A planthat does not include a hotel reservation may be more likely to be afirst-type plan, while plans that do may be less likely to be afirst-type plan, more likely to be a second-type plan, or both. A planreserved through a first source entity (e.g., a first travel website)may be more likely to be a first-type plan, while plans reserved througha second source entity (e.g., a second travel website) may be lesslikely to be a first-type plan, more likely to be a second-type plan, orboth.

In operation 330, the learning machine modifier module 230 modifies thelearning machine 120 (e.g., as previously trained in operation 320). Themodification of the learning machine 120 in operation 330 is based on apair of asymmetrical penalties (e.g., asymmetrical adverse or negativeweights) for incorrectly (e.g., erroneously, inaccurately, or wrongly)distinguishing between first and second types of plans. In some exampleembodiments, the penalty (e.g., a first penalty) for incorrectlyclassifying a plan as a first-type plan is greater than the penalty(e.g., a second penalty) for incorrectly classifying a plan as asecond-type plan. In other example embodiments, the penalty forincorrectly classifying a plan as a first-type plan is lesser than thepenalty for incorrectly classifying a plan as a second-type plan. Theasymmetrical penalties may be applicable to reference plans (e.g., whosetypes are known during training of the learning machine 120), candidateplans (e.g., whose types are to be determined at run-time), or both,according to various example embodiments.

In operation 340, the learning machine provider module 240 provides theoutput of operation 330, namely, the trained and modified learningmachine 120 for run-time use. This may be performed by enabling one ormore of the devices 130 and 150 to access the learning machine 120(e.g., via a user interface, such as a graphical user interface, or viaa programmatic interface, such as an application programming interface);marking the learning machine 120 as being ready, released, or otherwiseavailable for run-time use (e.g., as part of the network-based system105) in distinguishing between first and second types of plans;uploading or otherwise implementing a copy of the learning machine 120into the cloud 118 or other portion of the network-based system 105;providing a copy of the learning machine 120 to one or more of thedevices 130 and 150; or any suitable combination thereof.

As shown in FIG. 4 , in addition to any one or more of the operationspreviously described, the method 300 may include one or more ofoperations 420, 421, 422, 423, 424, 425, 426, 427, 430, and 431. One ormore of operations 420-427 may be performed as part (e.g., a precursortask, a subroutine, or a portion) of operation 320, in which thelearning machine trainer module 220 trains the learning machine 120 todistinguish between first and second types of plans (e.g., referenceplans, candidate plans, or both).

In operation 420, the training of the learning machine 120 is based ondecision trees constructed or otherwise generated by a Random Forests®technique. For example, such decision trees may be generated fromrandomly selected subsets of the reference metadata accessed inoperation 310. Such generated decision trees may be stored (e.g.,temporarily or permanently) in the database 115, in the trainer machine110, or in both. The randomly selected subsets of the reference metadatamay each describe a corresponding randomly selected portion of thereference plans (e.g., a randomly chosen subdivision of the referencetravel itineraries to which the reference metadata corresponds).Accordingly, performance of operation of 420 may include randomlyselecting portions of the reference plans that correspond to thereference metadata, randomly selecting subsets of the referencemetadata, generating decision trees from the randomly selected subsetsof the reference metadata or the corresponding reference metadata forthe randomly selected portions of the reference plans, training thelearning machine 120 based on the generated decision trees, or anysuitable combination thereof.

Operation 421 may be suitable where the reference plans are referencetravel plans, and the reference metadata of the reference travel plansindicates source entities that each reserved a corresponding referencetravel plan among the reference travel plans for a corresponding user.In operation 421, the training of the learning machine 120 todistinguish candidate first-type travel plans from candidate second-typetravel plans is based on the indicated source entities. Thus, where thesource entities are or include sources of travel bookings (e.g.,websites that offer travel bookings), such sources of travel bookingsmay influence the training of the learning machine 120 (e.g.,determining or otherwise fully or partially affecting weightings appliedto the decision trees).

Operation 422 may be suitable where the reference plans are referencetravel plans, and the reference metadata of the reference travel plansindicates sizes of destination airports. Each of the indicated sizes mayrespectively correspond to a different reference travel plan among thereference travel plans. In operation 422, the training of the learningmachine 120 to distinguish candidate first-type travel plans fromcandidate second-type travel plans is based on the indicated sizes ofthe destination airports. Thus, the sizes of destination airports mayinfluence the training of the learning machine 120 (e.g., determining orotherwise fully or partially affecting weightings applied to thedecision trees).

Operation 423 may be suitable where the reference plans are referencetravel plans, and the reference metadata of the reference travel plansindicates ratios of layovers to destination cities. Each of theindicated ratios may respectively correspond to a different referencetravel plan among the reference travel plans. In operation 423, thetraining of the learning machine 120 to distinguish candidate first-typetravel plans from candidate second-type travel plans is based on theindicated ratios of layovers to destination cities. Thus, the number oflayovers per destination may influence the training of the learningmachine 120 (e.g., determining or otherwise fully or partially affectingweightings applied to the decision trees).

Operation 424 may be suitable where the reference plans are referencetravel plans, and the reference metadata of the reference travel plansindicates total counts of destination cities. Each of the indicatedtotal counts may respectively correspond to a different reference travelplan among the reference travel plans. In operation 424, the training ofthe learning machine 120 to distinguish candidate first-type travelplans from candidate second-type travel plans is based on the indicatedtotal counts of destination cities. Thus, the total counts ofdestination cities may influence the training of the learning machine120 (e.g., determining or otherwise fully or partially affectingweightings applied to the decision trees).

Operation 425 may be suitable where the reference plans are referencetravel plans, and the reference metadata of the reference travel plansincludes indications of whether conventions occurred in destinationcities that each respectively corresponds to a different referencetravel plan among the reference travel plans. In operation 425, thetraining of the learning machine 120 to distinguish candidate first-typetravel plans from candidate second-type travel plans is based on theindications of whether conventions occurred in the destination cities.Thus, the indications of whether conventions co-occurred in destinationcities may influence the training of the learning machine 120 (e.g.,determining or otherwise fully or partially affecting weightings appliedto the decision trees).

Operation 426 may be suitable where the reference plans are referencetravel plans, and the reference metadata of the reference travel plansindicates dates of travel. Each reference travel plan among thereference travel plans respectively corresponds to a different set ofone or more dates of travel (e.g., a single date, or a pair or range ofdates). In operation 426, the learning machine trainer module 220accesses a curated database of annual first-type events whose dates ofoccurrence vary by year. The curated database may be stored in thedatabase 115 and accessed therefrom. Furthermore, in operation 426, thetraining of the learning machine 120 to distinguish candidate first-typetravel plans from candidate second-type travel plans is based on acomparison of the dates of occurrence to the dates of travel. Thus, suchannual first-type events may influence the training of the learningmachine 120 (e.g., determining or otherwise fully or partially affectingweightings applied to the decision trees).

Operation 427 may be suitable where the reference plans are referencetravel plans, and the reference metadata of the reference travel plansindicates dates of travel. Each reference travel plan among thereference travel plans respectively corresponds to a different set ofone or more dates of travel (e.g., a single date, or a pair or range ofdates). In operation 427, the learning machine trainer module 220accesses a curated database of annual second-type events whose dates ofoccurrence vary by year. The curated database may be stored in thedatabase 115 and accessed therefrom. Furthermore, in operation 427, thetraining of the learning machine 120 to distinguish candidate first-typetravel plans from candidate second-type travel plans is based on acomparison of the dates of occurrence to the dates of travel. Thus, suchannual second-type events may influence the training of the learningmachine 120 (e.g., determining or otherwise fully or partially affectingweightings applied to the decision trees).

As shown in FIG. 4 , one or both of operations 430 and 431 may beperformed as part of operation 330, in which the learning machinemodifier module 230 modifies the trained learning machine 120 based onasymmetrical penalties discussed above.

In operation 430, the learning machine modifier module 230 applies afirst penalty (e.g., in a pair of unequal and asymmetrical penalties)for incorrectly classifying a plan as a first-type plan. This firstpenalty is applied to the trained learning machine 120 (e.g., in theform of weighting one or more decision trees with a first mathematicalweighting factor that penalizes erroneous classifications into the firsttype of plan).

In operation 431, the learning machine modifier module 230 applies asecond penalty (e.g., in the pair of unequal and asymmetrical penalties)for incorrectly classifying a plan as a second-type plan. This secondpenalty is applied to the trained learning machine 120 (e.g., in theform of weighting one or more decision trees with a second mathematicalweighting factor that penalizes erroneous classifications into thesecond type of plan).

With respect to operations 430 and 431, the first penalty is moresignificant (e.g., larger in absolute value) than the second penalty insome example embodiments. This may have the effect of biasing themodified learning machine 120 to be more aggressive in classifying plansinto the second type, and more conservative in classifying plans intothe first type. Conversely, in alternative example embodiments, thefirst penalty is less significant than the second penalty. This may havethe effect of biasing the modified learning machine 120 toward beingmore conservative in classifying plans into the second type, and moreaggressive in classifying plans into the first type.

According to various example embodiments, one or more of themethodologies described herein may facilitate training a learningmachine. Moreover, one or more of the methodologies described herein mayfacilitate training a learning machine to distinguish first-type plansfrom second-type plans. Hence, one or more of the methodologiesdescribed herein may facilitate automatically distinguishing candidatefirst-type plans from candidate second-type plans based on asymmetricalpenalties for incorrectly distinguishing candidate first-type plans fromcandidate second-type plans, as well as consistently making suchdistinction across numerous instances of candidate plans, compared tocapabilities of pre-existing systems and methods.

When these effects are considered in aggregate, one or more of themethodologies described herein may obviate a need for certain efforts orresources that otherwise would be involved in classifying first-typeplans and second-type plans. Efforts expended by a user in determiningcorrect classifications of plans may be reduced by use of (e.g.,reliance upon) a special-purpose machine that implements one or more ofthe methodologies described herein. Computing resources used by one ormore systems or machines (e.g., within the network environment 100) maysimilarly be reduced (e.g., compared to systems or machines that lackthe structures discussed herein or are otherwise unable to perform thefunctions discussed herein). Examples of such computing resourcesinclude processor cycles, network traffic, computational capacity, mainmemory usage, graphics rendering capacity, graphics memory usage, datastorage capacity, power consumption, and cooling capacity.

FIG. 5 is a block diagram illustrating components of a machine 500,according to some example embodiments, able to read instructions 524from a machine-readable medium 522 (e.g., a non-transitorymachine-readable medium, a machine-readable storage medium, acomputer-readable storage medium, or any suitable combination thereof)and perform any one or more of the methodologies discussed herein, inwhole or in part. Specifically, FIG. 5 shows the machine 500 in theexample form of a computer system (e.g., a computer) within which theinstructions 524 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 500 to performany one or more of the methodologies discussed herein may be executed,in whole or in part.

In alternative embodiments, the machine 500 operates as a standalonedevice or may be communicatively coupled (e.g., networked) to othermachines. In a networked deployment, the machine 500 may operate in thecapacity of a server machine or a client machine in a server-clientnetwork environment, or as a peer machine in a distributed (e.g.,peer-to-peer) network environment. The machine 500 may be a servercomputer, a client computer, a personal computer (PC), a tabletcomputer, a laptop computer, a netbook, a cellular telephone, a smartphone, a set-top box (STB), a personal digital assistant (PDA), a webappliance, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 524, sequentially orotherwise, that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute the instructions 524 to perform all or part of any oneor more of the methodologies discussed herein.

The machine 500 includes a processor 502 (e.g., one or more centralprocessing units (CPUs), one or more graphics processing units (GPUs),one or more digital signal processors (DSPs), one or more applicationspecific integrated circuits (ASICs), one or more radio-frequencyintegrated circuits (RFICs), or any suitable combination thereof), amain memory 504, and a static memory 506, which are configured tocommunicate with each other via a bus 508. The processor 502 containssolid-state digital microcircuits (e.g., electronic, optical, or both)that are configurable, temporarily or permanently, by some or all of theinstructions 524 such that the processor 502 is configurable to performany one or more of the methodologies described herein, in whole or inpart. For example, a set of one or more microcircuits of the processor502 may be configurable to execute one or more modules (e.g., softwaremodules) described herein. In some example embodiments, the processor502 is a multicore CPU (e.g., a dual-core CPU, a quad-core CPU, an8-core CPU, or a 128-core CPU) within which each of multiple coresbehaves as a separate processor that is able to perform any one or moreof the methodologies discussed herein, in whole or in part. Although thebeneficial effects described herein may be provided by the machine 500with at least the processor 502, these same beneficial effects may beprovided by a different kind of machine that contains no processors(e.g., a purely mechanical system, a purely hydraulic system, or ahybrid mechanical-hydraulic system), if such a processor-less machine isconfigured to perform one or more of the methodologies described herein.

The machine 500 may further include a graphics display 510 (e.g., aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, a cathode ray tube (CRT), orany other display capable of displaying graphics or video). The machine500 may also include an alphanumeric input device 512 (e.g., a keyboardor keypad), a pointer input device 514 (e.g., a mouse, a touchpad, atouchscreen, a trackball, a joystick, a stylus, a motion sensor, an eyetracking device, a data glove, or other pointing instrument), a datastorage 516, an audio generation device 518 (e.g., a sound card, anamplifier, a speaker, a headphone jack, or any suitable combinationthereof), and a network interface device 520.

The data storage 516 (e.g., a data storage device) includes themachine-readable medium 522 (e.g., a tangible and non-transitorymachine-readable storage medium) on which are stored the instructions524 embodying any one or more of the methodologies or functionsdescribed herein. The instructions 524 may also reside, completely or atleast partially, within the main memory 504, within the static memory506, within the processor 502 (e.g., within the processor's cachememory), or any suitable combination thereof, before or during executionthereof by the machine 500. Accordingly, the main memory 504, the staticmemory 506, and the processor 502 may be considered machine-readablemedia (e.g., tangible and non-transitory machine-readable media). Theinstructions 524 may be transmitted or received over the network 190 viathe network interface device 520. For example, the network interfacedevice 520 may communicate the instructions 524 using any one or moretransfer protocols (e.g., hypertext transfer protocol (HTTP)).

In some example embodiments, the machine 500 may be a portable computingdevice (e.g., a smart phone, a tablet computer, or a wearable device)and may have one or more additional input components 530 (e.g., sensorsor gauges). Examples of such input components 530 include an image inputcomponent (e.g., one or more cameras), an audio input component (e.g.,one or more microphones), a direction input component (e.g., a compass),a location input component (e.g., a global positioning system (GPS)receiver), an orientation component (e.g., a gyroscope), a motiondetection component (e.g., one or more accelerometers), an altitudedetection component (e.g., an altimeter), a temperature input component(e.g., a thermometer), and a gas detection component (e.g., a gassensor). Input data gathered by any one or more of these inputcomponents 530 may be accessible and available for use by any of themodules described herein (e.g., with suitable privacy notifications andprotections, such as opt-in consent or opt-out consent, implemented inaccordance with user preference, applicable regulations, or any suitablecombination thereof).

As used herein, the term “memory” refers to a machine-readable mediumable to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 522 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions. The term “machine-readable medium” shall also be taken toinclude any medium, or combination of multiple media, that is capable ofcarrying (e.g., storing or communicating) the instructions 524 forexecution by the machine 500, such that the instructions 524, whenexecuted by one or more processors of the machine 500 (e.g., processor502), cause the machine 500 to perform any one or more of themethodologies described herein, in whole or in part. Accordingly, a“machine-readable medium” refers to a single storage apparatus ordevice, as well as cloud-based storage systems or storage networks thatinclude multiple storage apparatus or devices. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, one or more tangible and non-transitory data repositories(e.g., data volumes) in the example form of a solid-state memory chip,an optical disc, a magnetic disc, or any suitable combination thereof.

A “non-transitory” machine-readable medium, as used herein, specificallyexcludes propagating signals per se. According to various exampleembodiments, the instructions 524 for execution by the machine 500 canbe communicated via a carrier medium (e.g., a machine-readable carriermedium). Examples of such a carrier medium include a non-transientcarrier medium (e.g., a non-transitory machine-readable storage medium,such as a solid-state memory that is physically movable from one placeto another place) and a transient carrier medium (e.g., a carrier waveor other propagating signal that communicates the instructions 524).

Certain example embodiments are described herein as including modules.Modules may constitute software modules (e.g., code stored or otherwiseembodied in a machine-readable medium or in a transmission medium),hardware modules, or any suitable combination thereof. A “hardwaremodule” is a tangible (e.g., non-transitory) physical component (e.g., aset of one or more processors) capable of performing certain operationsand may be configured or arranged in a certain physical manner. Invarious example embodiments, one or more computer systems or one or morehardware modules thereof may be configured by software (e.g., anapplication or portion thereof) as a hardware module that operates toperform operations described herein for that module.

In some example embodiments, a hardware module may be implementedmechanically, electronically, hydraulically, or any suitable combinationthereof. For example, a hardware module may include dedicated circuitryor logic that is permanently configured to perform certain operations. Ahardware module may be or include a special-purpose processor, such as afield programmable gate array (FPGA) or an ASIC. A hardware module mayalso include programmable logic or circuitry that is temporarilyconfigured by software to perform certain operations. As an example, ahardware module may include software encompassed within a CPU or otherprogrammable processor. It will be appreciated that the decision toimplement a hardware module mechanically, hydraulically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity that may be physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Furthermore, as used herein, the phrase“hardware-implemented module” refers to a hardware module. Consideringexample embodiments in which hardware modules are temporarily configured(e.g., programmed), each of the hardware modules need not be configuredor instantiated at any one instance in time. For example, where ahardware module includes a CPU configured by software to become aspecial-purpose processor, the CPU may be configured as respectivelydifferent special-purpose processors (e.g., each included in a differenthardware module) at different times. Software (e.g., a software module)may accordingly configure one or more processors, for example, to becomeor otherwise constitute a particular hardware module at one instance oftime and to become or otherwise constitute a different hardware moduleat a different instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over circuits and buses) between oramong two or more of the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory (e.g., a memory device) to which itis communicatively coupled. A further hardware module may then, at alater time, access the memory to retrieve and process the stored output.Hardware modules may also initiate communications with input or outputdevices, and can operate on a resource (e.g., a collection ofinformation from a computing resource).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module in which the hardware includes one or more processors.Accordingly, the operations described herein may be at least partiallyprocessor-implemented, hardware-implemented, or both, since a processoris an example of hardware, and at least some operations within any oneor more of the methods discussed herein may be performed by one or moreprocessor-implemented modules, hardware-implemented modules, or anysuitable combination thereof.

Moreover, such one or more processors may perform operations in a “cloudcomputing” environment or as a service (e.g., within a “software as aservice” (SaaS) implementation). For example, at least some operationswithin any one or more of the methods discussed herein may be performedby a group of computers (e.g., as examples of machines that includeprocessors), with these operations being accessible via a network (e.g.,the Internet) and via one or more appropriate interfaces (e.g., anapplication program interface (API)). The performance of certainoperations may be distributed among the one or more processors, whetherresiding only within a single machine or deployed across a number ofmachines. In some example embodiments, the one or more processors orhardware modules (e.g., processor-implemented modules) may be located ina single geographic location (e.g., within a home environment, an officeenvironment, or a server farm). In other example embodiments, the one ormore processors or hardware modules may be distributed across a numberof geographic locations.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures and theirfunctionality presented as separate components and functions in exampleconfigurations may be implemented as a combined structure or componentwith combined functions. Similarly, structures and functionalitypresented as a single component may be implemented as separatecomponents and functions. These and other variations, modifications,additions, and improvements fall within the scope of the subject matterherein.

Some portions of the subject matter discussed herein may be presented interms of algorithms or symbolic representations of operations on datastored as bits or binary digital signals within a memory (e.g., acomputer memory or other machine memory). Such algorithms or symbolicrepresentations are examples of techniques used by those of ordinaryskill in the data processing arts to convey the substance of their workto others skilled in the art. As used herein, an “algorithm” is aself-consistent sequence of operations or similar processing leading toa desired result. In this context, algorithms and operations involvephysical manipulation of physical quantities. Typically, but notnecessarily, such quantities may take the form of electrical, magnetic,or optical signals capable of being stored, accessed, transferred,combined, compared, or otherwise manipulated by a machine. It isconvenient at times, principally for reasons of common usage, to referto such signals using words such as “data,” “content,” “bits,” “values,”“elements,” “symbols,” “characters,” “terms,” “numbers,” “numerals,” orthe like. These words, however, are merely convenient labels and are tobe associated with appropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “accessing,” “processing,” “detecting,” “computing,”“calculating,” “determining,” “generating,” “presenting,” “displaying,”or the like refer to actions or processes performable by a machine(e.g., a computer) that manipulates or transforms data represented asphysical (e.g., electronic, magnetic, or optical) quantities within oneor more memories (e.g., volatile memory, non-volatile memory, or anysuitable combination thereof), registers, or other machine componentsthat receive, store, transmit, or display information. Furthermore,unless specifically stated otherwise, the terms “a” or “an” are hereinused, as is common in patent documents, to include one or more than oneinstance. Finally, as used herein, the conjunction “or” refers to anon-exclusive “or,” unless specifically stated otherwise.

The following enumerated descriptions describe various examples ofmethods, machine-readable media, and systems (e.g., machines, devices,or other apparatus) discussed herein.

A first example provides a method comprising:

-   -   accessing, by one or more processors, a training database of        reference metadata descriptive of reference travel plans that        include reference first-type travel plans (e.g., personal,        non-tracked, non-reimbursable, or non-business travel plans) and        reference second-type travel plans (e.g., non-personal, tracked,        reimbursable, or business travel plans);    -   training, by the one or more processors, a learning machine to        distinguish candidate first-type travel plans from candidate        second-type travel plans, the learning machine being trained        based on decision trees generated from randomly selected subsets        of the reference metadata that is descriptive of the reference        travel plans, the randomly selected subsets each describing a        corresponding randomly selected portion of the reference travel        plans that include the reference first-type travel plans and the        reference second-type travel plans;    -   modifying, by the one or more processors, the trained learning        machine based on asymmetrical penalties for incorrectly        distinguishing candidate first-type travel plans from candidate        second-type travel plans; and    -   providing, by the one or more processors, the modified learning        machine trained to distinguish candidate first-type travel plans        from candidate second-type travel plans based on the        asymmetrical penalties for incorrectly distinguishing candidate        first-type travel plans from candidate second-type travel plans.        According to such a method, the trainer machine 110 may train,        modify, and provide the learning machine 120 (e.g., for run-time        access by the devices 130 and 150).

A second example provides a method according to the first example,wherein:

-   -   the asymmetrical penalties include unequal first and second        penalties, the first penalty to be applied for incorrectly        classifying a candidate first-type travel plan being greater        than the second penalty to be applied for incorrectly        classifying a candidate second-type travel plan. Thus,        misclassifying a travel plan as a first-type travel plan would        incur a larger penalty than misclassifying it as a second-type        travel plan.

A third example provides a method according to the first example,wherein:

-   -   the asymmetrical penalties include unequal first and second        penalties, the first penalty to be applied for incorrectly        classifying a candidate first-type travel plan being less than        the second penalty to be applied for incorrectly classifying a        candidate second-type travel plan. Thus, misclassifying a travel        plan as a second-type travel plan would incur a larger penalty        than misclassifying it as a first-type travel plan.

A fourth example provides a method according to any one of the first tothird examples, wherein:

-   -   the reference metadata of the reference travel plans indicates        source entities that each reserved a corresponding reference        travel plan among the reference travel plans for a corresponding        user; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on the indicated source entities that each reserved a        corresponding reference travel plan for a corresponding user.        Thus, where the source entities are or include sources of travel        bookings (e.g., web sites that offer travel bookings), such        sources of travel bookings may influence the training of the        learning machine (e.g., determining or otherwise fully or        partially affecting weightings applied to the decision trees).        For example, a first source (e.g., a first travel website) may        be more influential than a second source (e.g., a second travel        website).

A fifth example provides a method according to any one of the firstthrough fourth examples, wherein:

-   -   the reference metadata of the reference travel plans indicates        sizes of destination airports, the indicated sizes respectively        corresponding to each reference travel plan among the reference        travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on the indicated sizes of the destination airports.        Thus, the sizes of destination airports may influence the        training of the learning machine (e.g., determining or otherwise        fully or partially affecting weightings applied to the decision        trees). For example, large airport sizes (e.g., above a        threshold percentile in size, runways, runway length, flights        per day, or gates) may be correlated with first-type travel        plans, while small airport sizes (e.g., below the threshold        percentile in size, runways, runway length, flights per day, or        gates) may be correlated with second-type travel plans, or vice        versa.

A sixth example provides a method according to any of the first throughfifth examples, wherein:

-   -   the reference metadata of the reference travel plans indicates        ratios of layovers to destination cities, the indicated ratios        respectively corresponding to each reference travel plan among        the reference travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on the indicated ratios of layovers to destination        cities. Thus, the number of layovers per destination may        influence the training of the learning machine (e.g.,        determining or otherwise fully or partially affecting weightings        applied to the decision trees). For example, a small number of        layovers per destination (e.g., 0 or 1) may be correlated with        second-type travel plans, while a large number of layovers per        destination (e.g., 2+) may be correlated with first-type travel        plans, or vice versa.

A seventh example provides a method according to any of the firstthrough sixth examples, wherein:

-   -   the reference metadata of the reference travel plans indicates        total counts of destination cities, the indicated total counts        respectively corresponding to each reference travel plan among        the reference travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on the indicated total counts of destination cities.        Thus, the total counts of destination cities may influence the        training of the learning machine (e.g., determining or otherwise        fully or partially affecting weightings applied to the decision        trees). For example, large total counts of destination cities        may be correlated with first-type travel plans, while small        total counts of destination cities may be correlated with        second-type travel plans, or vice versa.

An eighth example provides a method according to any of the firstthrough seventh examples, wherein:

-   -   the reference metadata of the reference travel plans includes        indications of whether conventions occurred in destination        cities respectively corresponding to each reference travel plan        among the reference travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on the indications of whether conventions occurred in        the destination cities. Thus, the indications of whether        conventions co-occurred in destination cities may influence the        training of the learning machine (e.g., determining or otherwise        fully or partially affecting weightings applied to the decision        trees). For example, occurrence of conventions may be correlated        with second-type travel plans, while non-occurrence of        conventions may be correlated with first-type travel plans. As        another example, occurrence of first-type conventions may be        correlated with first-type travel plans, while occurrence of        second-type conventions may be correlated with second-type        travel plans.

A ninth example provides a method according to any of the first througheighth examples, wherein:

-   -   accessing a curated database of annual first-type events whose        dates of occurrence vary by year; and wherein:    -   the reference metadata of the reference travel plans indicates        dates of travel respectively corresponding to each reference        travel plan among the reference travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on a comparison of the dates of occurrence to the dates        of travel. Thus, such annual first-type events may influence the        training of the learning machine (e.g., determining or otherwise        fully or partially affecting weightings applied to the decision        trees). For example, travel plans during the Thanksgiving        holidays or during Lunar New Year periods may be more likely to        be first-type travel plans, while travel plans outside any of        the annual first-type events tracked in the curated database may        be more likely to be second-type travel plans.

A tenth example provides a method according to any of the first throughninth examples, wherein:

-   -   accessing a curated database of annual second-type events whose        dates of occurrence vary by year; and wherein:    -   the reference metadata of the reference travel plans indicates        dates of travel respectively corresponding to each reference        travel plan among the reference travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on a comparison of the dates of occurrence to the dates        of travel. Thus, such annual second-type events may influence        the training of the learning machine (e.g., determining or        otherwise fully or partially affecting weightings applied to the        decision trees). For example, travel plans during a yearly trade        show (e.g., Consumer Electronics Show (CES)) may be more likely        to be second-type travel plans, while travel plans outside any        of the annual second-type events tracked in the curated database        may be more likely to be first-type travel plans.

An eleventh example provides a system (e.g., a computer system fortraining a learning machine) comprising:

-   -   one or more processors; and    -   a memory storing instructions that, when executed by at least        one processor among the one or more processors, cause the system        to perform operations comprising:    -   accessing a training database of reference metadata descriptive        of reference travel plans that include reference first-type        travel plans and reference second-type travel plans;    -   training a learning machine to distinguish candidate first-type        travel plans from candidate second-type travel plans, the        learning machine being trained based on decision trees generated        from randomly selected subsets of the reference metadata that is        descriptive of the reference travel plans, the randomly selected        subsets each describing a corresponding randomly selected        portion of the reference travel plans that include the reference        first-type travel plans and the reference second-type travel        plans;    -   modifying the trained learning machine based on asymmetrical        penalties for incorrectly distinguishing candidate first-type        travel plans from candidate second-type travel plans; and    -   providing the modified learning machine trained to distinguish        candidate first-type travel plans from candidate second-type        travel plans based on the asymmetrical penalties for incorrectly        distinguishing candidate first-type travel plans from candidate        second-type travel plans.

A twelfth example provides a system according to the eleventh example,wherein:

-   -   the asymmetrical penalties include unequal first and second        penalties, the first penalty to be applied for incorrectly        classifying a candidate first-type travel plan being greater        than the second penalty to be applied for incorrectly        classifying a candidate second-type travel plan.

A thirteenth example provides a system according to the eleventh exampleor the twelfth example, wherein:

-   -   the reference metadata of the reference travel plans indicates        source entities that each reserved a corresponding reference        travel plan among the reference travel plans for a corresponding        user; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on the indicated source entities that each reserved a        corresponding reference travel plan for a corresponding user.

A fourteenth example provides a system according to any of the eleventhto thirteenth examples, wherein:

-   -   the reference metadata of the reference travel plans indicates        ratios of layovers to destination cities, the indicated ratios        respectively corresponding to each reference travel plan among        the reference travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on the indicated ratios of layovers to destination        cities.

A fifteenth example provides a system according to any of the elevenththrough fourteenth examples, wherein:

-   -   the reference metadata of the reference travel plans includes        indications of whether conventions occurred in destination        cities respectively corresponding to each reference travel plan        among the reference travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on the indications of whether conventions occurred in        the destination cities.

A sixteenth example provides a machine-readable medium (e.g., anon-transitory machine-readable storage medium) comprising instructionsthat, when executed by one or more processors of a machine, cause themachine to perform operations comprising:

-   -   accessing a training database of reference metadata descriptive        of reference travel plans that include reference first-type        travel plans and reference second-type travel plans;    -   training a learning machine to distinguish candidate first-type        travel plans from candidate second-type travel plans, the        learning machine being trained based on decision trees generated        from randomly selected subsets of the reference metadata that is        descriptive of the reference travel plans, the randomly selected        subsets each describing a corresponding randomly selected        portion of the reference travel plans that include the reference        first-type travel plans and the reference second-type travel        plans;    -   modifying the trained learning machine based on asymmetrical        penalties for incorrectly distinguishing candidate first-type        travel plans from candidate second-type travel plans; and    -   providing the modified trained learning machine trained to        distinguish candidate first-type travel plans from candidate        second-type travel plans based on the asymmetrical penalties for        incorrectly distinguishing candidate first-type travel plans        from candidate second-type travel plans.

A seventeenth example provides a machine-readable medium according tothe sixteenth example, wherein:

-   -   the asymmetrical penalties include unequal first and second        penalties, the first penalty to be applied for incorrectly        classifying a candidate first-type travel plan being greater        than the second penalty to be applied for incorrectly        classifying a candidate second-type travel plan.

An eighteenth example provides a machine-readable medium according tothe sixteenth example or the seventeenth example, wherein:

-   -   the reference metadata of the reference travel plans indicates        sizes of destination airports, the indicated sizes respectively        corresponding to each reference travel plan among the reference        travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on the indicated sizes of the destination airports.

A nineteenth example provides a machine-readable medium according to anyof the sixteenth through eighteenth examples, wherein:

-   -   the reference metadata of the reference travel plans indicates        total counts of destination cities, the indicated total counts        respectively corresponding to each reference travel plan among        the reference travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on the indicated total counts of destination cities.

A twentieth example provides a machine-readable medium according to anyof the sixteenth to nineteenth examples, wherein the operations furthercomprise:

-   -   accessing a curated database of annual second-type events whose        dates of occurrence vary by year; and wherein:    -   the reference metadata of the reference travel plans indicates        dates of travel respectively corresponding to each reference        travel plan among the reference travel plans; and    -   the training of the learning machine to distinguish candidate        first-type travel plans from candidate second-type travel plans        is based on a comparison of the dates of occurrence to the dates        of travel.

A twenty-first example provides a method comprising:

-   -   accessing, by one or more processors, a candidate travel plan to        be classified by a learning machine trained to distinguish        candidate first-type travel plans (e.g., personal, non-tracked,        non-reimbursable, or non-business travel plans) from candidate        second-type travel plans (e.g., non-personal, tracked,        reimbursable, or business travel plans);    -   accessing, by the one or more processors, the learning machine        trained to distinguish candidate first-type travel plans from        candidate second-type travel plans, the learning machine being        trained based on decision trees generated from randomly selected        subsets of reference metadata that is descriptive of reference        travel plans that include reference first-type travel plans and        reference second-type travel plans, the randomly selected        subsets each describing a corresponding randomly selected        portion of the reference travel plans, the trained learning        machine being modified based on asymmetrical penalties for        incorrectly distinguishing candidate first-type travel plans        from candidate second-type travel plans;    -   inputting, by the one or more processors, candidate metadata of        the candidate travel plan to the learning machine trained to        distinguish candidate first-type travel plans from candidate        second-type travel plans and modified based on the asymmetrical        penalties for incorrectly distinguishing candidate first-type        travel plans from candidate second-type travel plans; and    -   causing, by the one or more processors, presentation of a        classification that indicates a type of the candidate travel        plan, the indicated type being output from the learning machine        in response to the inputting of the candidate metadata of the        candidate travel plan. According to such a method, a device        (e.g., device 130) may access and use the trained and modified        learning machine 120 to classify a candidate travel plan and        provide an indication of its type.

A twenty-second example provides a method according to the twenty-firstexample, wherein:

-   -   the candidate metadata of the candidate travel plan indicates a        source entity that reserved the candidate travel plan for a        corresponding user; and    -   the learning machine outputs the type of the candidate travel        plan based on the source entity that reserved the candidate        travel plan for the corresponding user.

A twenty-third example provides a method according to the twenty-firstor twenty-second example, wherein:

-   -   the candidate metadata of the candidate travel plan indicates a        size of a destination airport corresponding to the candidate        travel plan; and    -   the learning machine outputs the type of the candidate travel        plan based on the size of the destination airport corresponding        to the candidate travel plan.

A twenty-fourth example provides a method according to any of thetwenty-first through twenty-third examples, wherein:

-   -   the candidate metadata of the candidate travel plan indicates a        ratio of layovers to destination cities corresponding to the        candidate travel plan; and    -   the learning machine outputs the type of the candidate travel        plan based on the ratio of layovers to destination cities        corresponding to the candidate travel plan.

A twenty-fifth example provides a method according to any of thetwenty-first through twenty-fourth examples, wherein:

-   -   the candidate metadata of the candidate travel plan indicates a        total count of destination cities corresponding to the candidate        travel plan; and    -   the learning machine outputs the type of the candidate travel        plan based on the total count of destination cities        corresponding to the candidate travel plan.

A twenty-sixth example provides a method according to any of thetwenty-first through twenty-fifth examples, wherein:

-   -   the candidate metadata of the candidate travel plan includes an        indication of whether a convention occurred in a destination        city corresponding to the candidate travel plan; and    -   the learning machine outputs the type of the candidate travel        plan based on the indication of whether the convention occurred        in the destination city corresponding to the candidate travel        plan.

A twenty-seventh example provides a method according to any of thetwenty-first through twenty-sixth examples, wherein:

-   -   the candidate metadata of the candidate travel plan indicates        one or more dates of travel corresponding to the candidate        travel plan; and    -   the learning machine outputs the type of the candidate travel        plan based on the one or more dates of travel corresponding to        the candidate travel plan.

A twenty-eighth example provides a system (e.g., a computer system fortraining a learning machine) comprising:

-   -   one or more processors; and    -   a memory storing instructions that, when executed by at least        one processor among the one or more processors, cause the system        to perform operations comprising:    -   accessing a candidate travel plan to be classified by a learning        machine trained to distinguish candidate first-type travel plans        from candidate second-type travel plans;    -   accessing the learning machine trained to distinguish candidate        first-type travel plans from candidate second-type travel plans,        the learning machine being trained based on decision trees        generated from randomly selected subsets of reference metadata        that is descriptive of reference travel plans that include        reference first-type travel plans and reference second-type        travel plans, the randomly selected subsets each describing a        corresponding randomly selected portion of the reference travel        plans, the trained learning machine being modified based on        asymmetrical penalties for incorrectly distinguishing candidate        first-type travel plans from candidate second-type travel plans;    -   inputting candidate metadata of the candidate travel plan to the        learning machine trained to distinguish candidate first-type        travel plans from candidate second-type travel plans and        modified based on the asymmetrical penalties for incorrectly        distinguishing candidate first-type travel plans from candidate        second-type travel plans; and    -   causing presentation of a classification that indicates a type        of the candidate travel plan, the indicated type being output        from the learning machine in response to the inputting of the        candidate metadata of the candidate travel plan.

A twenty-ninth example provides a machine-readable medium (e.g., anon-transitory machine-readable storage medium) comprising instructionsthat, when executed by one or more processors of a machine, cause themachine to perform operations comprising:

-   -   accessing a candidate travel plan to be classified by a learning        machine trained to distinguish candidate first-type travel plans        from candidate second-type travel plans;    -   accessing the learning machine trained to distinguish candidate        first-type travel plans from candidate second-type travel plans,        the learning machine being trained based on decision trees        generated from randomly selected subsets of reference metadata        that is descriptive of reference travel plans that include        reference first-type travel plans and reference second-type        travel plans, the randomly selected subsets each describing a        corresponding randomly selected portion of the reference travel        plans, the trained learning machine being modified based on        asymmetrical penalties for incorrectly distinguishing candidate        first-type travel plans from candidate second-type travel plans;    -   inputting candidate metadata of the candidate travel plan to the        learning machine trained to distinguish candidate first-type        travel plans from candidate second-type travel plans and        modified based on the asymmetrical penalties for incorrectly        distinguishing candidate first-type travel plans from candidate        second-type travel plans; and    -   causing presentation of a classification that indicates a type        of the candidate travel plan, the indicated type being output        from the learning machine in response to the inputting of the        candidate metadata of the candidate travel plan.

A thirtieth example provides a carrier medium carrying machine-readableinstructions for controlling a machine to carry out the operations(e.g., method operations) performed in any one of the previouslydescribed examples.

What is claimed is:
 1. A method comprising: accessing, by one or moreprocessors, a training database of reference metadata descriptive ofreference travel plans that include reference first-type travel plansand reference second-type travel plans; training, by the one or moreprocessors, a learning machine to distinguish candidate first-typetravel plans from candidate second-type travel plans, the candidatefirst-type travel plans comprising non-reimbursable non-business travelplans, the candidate second-type travel plans comprising reimbursablebusiness travel plans, the learning machine being trained based ondecision trees generated from randomly selected subsets of the referencemetadata that is descriptive of the reference travel plans, the randomlyselected subsets each describing a corresponding randomly selectedportion of the reference travel plans that include the referencefirst-type travel plans and the reference second-type travel plans;modifying, by the one or more processors, the trained learning machinebased on asymmetrical penalties for incorrectly distinguishing candidatefirst-type travel plans from candidate second-type travel plans, theasymmetrical penalties including unequal first and second penalties, thefirst penalty to be applied for incorrectly classifying a candidatefirst-type travel plan being greater than the second penalty to beapplied for incorrectly classifying a candidate second-type travel plan;and providing, by the one or more processors, the modified learningmachine trained to distinguish candidate first-type travel plans fromcandidate second-type travel plans based on the asymmetrical penaltiesfor incorrectly distinguishing candidate first-type travel plans fromcandidate second-type travel plans.
 2. The method of claim 1, wherein:the reference metadata of the reference travel plans indicates sourceentities that each reserved a corresponding reference travel plan amongthe reference travel plans for a corresponding user; and the training ofthe learning machine to distinguish candidate first-type travel plansfrom candidate second-type travel plans is based on the indicated sourceentities that each reserved a corresponding reference travel plan for acorresponding user.
 3. The method of claim 1, wherein: the referencemetadata of the reference travel plans indicates sizes of destinationairports, the indicated sizes respectively corresponding to eachreference travel plan among the reference travel plans; and the trainingof the learning machine to distinguish candidate first-type travel plansfrom candidate second-type travel plans is based on the indicated sizesof the destination airports.
 4. The method of claim 1, wherein: thereference metadata of the reference travel plans indicates ratios oflayovers to destination cities, the indicated ratios respectivelycorresponding to each reference travel plan among the reference travelplans; and the training of the learning machine to distinguish candidatefirst-type travel plans from candidate second-type travel plans is basedon the indicated ratios of layovers to destination cities.
 5. The methodof claim 1, wherein: the reference metadata of the reference travelplans indicates total counts of destination cities, the indicated totalcounts respectively corresponding to each reference travel plan amongthe reference travel plans; and the training of the learning machine todistinguish candidate first-type travel plans from candidate second-typetravel plans is based on the indicated total counts of destinationcities.
 6. The method of claim 1, wherein: the reference metadata of thereference travel plans includes indications of whether conventionsoccurred in destination cities respectively corresponding to eachreference travel plan among the reference travel plans; and the trainingof the learning machine to distinguish candidate first-type travel plansfrom candidate second-type travel plans is based on the indications ofwhether conventions occurred in the destination cities.
 7. The method ofclaim 1, further comprising: accessing a curated database of annualfirst-type events whose dates of occurrence vary by year; and wherein:the reference metadata of the reference travel plans indicates dates oftravel respectively corresponding to each reference travel plan amongthe reference travel plans; and the training of the learning machine todistinguish candidate first-type travel plans from candidate second-typetravel plans is based on a comparison of the dates of occurrence to thedates of travel.
 8. The method of claim 1, wherein: accessing a curateddatabase of annual second-type events whose dates of occurrence vary byyear; and wherein: the reference metadata of the reference travel plansindicates dates of travel respectively corresponding to each referencetravel plan among the reference travel plans; and the training of thelearning machine to distinguish candidate first-type travel plans fromcandidate second-type travel plans is based on a comparison of the datesof occurrence to the dates of travel.
 9. A system comprising: one ormore processors; and a memory storing instructions that, when executedby at least one processor among the one or more processors, cause thesystem to perform operations comprising: accessing a training databaseof reference metadata descriptive of reference travel plans that includereference first-type travel plans and reference second-type travelplans; training a learning machine to distinguish candidate first-typetravel plans from candidate second-type travel plans, the candidatefirst-type travel plans comprising non-reimbursable non-business travelplans, the candidate second-type travel plans comprising reimbursablebusiness travel plans, the learning machine being trained based ondecision trees generated from randomly selected subsets of the referencemetadata that is descriptive of the reference travel plans, the randomlyselected subsets each describing a corresponding randomly selectedportion of the reference travel plans that include the referencefirst-type travel plans and the reference second-type travel plans;modifying the trained learning machine based on asymmetrical penaltiesfor incorrectly distinguishing candidate first-type travel plans fromcandidate second-type travel plans, the asymmetrical penalties includingunequal first and second penalties, the first penalty to be applied forincorrectly classifying a candidate first-type travel plan being greaterthan the second penalty to be applied for incorrectly classifying acandidate second-type travel plan; and providing the modified learningmachine trained to distinguish candidate first-type travel plans fromcandidate second-type travel plans based on the asymmetrical penaltiesfor incorrectly distinguishing candidate first-type travel plans fromcandidate second-type travel plans.
 10. The system of claim 9, wherein:the reference metadata of the reference travel plans indicates sourceentities that each reserved a corresponding reference travel plan amongthe reference travel plans for a corresponding user; and the training ofthe learning machine to distinguish candidate first-type travel plansfrom candidate second-type travel plans is based on the indicated sourceentities that each reserved a corresponding reference travel plan for acorresponding user.
 11. The system of claim 9, wherein: the referencemetadata of the reference travel plans indicates ratios of layovers todestination cities, the indicated ratios respectively corresponding toeach reference travel plan among the reference travel plans; and thetraining of the learning machine to distinguish candidate first-typetravel plans from candidate second-type travel plans is based on theindicated ratios of layovers to destination cities.
 12. The system ofclaim 9, wherein: the reference metadata of the reference travel plansincludes indications of whether conventions occurred in destinationcities respectively corresponding to each reference travel plan amongthe reference travel plans; and the training of the learning machine todistinguish candidate first-type travel plans from candidate second-typetravel plans is based on the indications of whether conventions occurredin the destination cities.
 13. A non-transitory machine-readable storagemedium comprising instructions that, when executed by one or moreprocessors of a machine, cause the machine to perform operationscomprising: accessing a training database of reference metadatadescriptive of reference travel plans that include reference first-typetravel plans and reference second-type travel plans; training a learningmachine to distinguish candidate first-type travel plans from candidatesecond-type travel plans, the candidate first-type travel planscomprising non-reimbursable non-business travel plans, the candidatesecond-type travel plans comprising reimbursable business travel plans,the learning machine being trained based on decision trees generatedfrom randomly selected subsets of the reference metadata that isdescriptive of the reference travel plans, the randomly selected subsetseach describing a corresponding randomly selected portion of thereference travel plans that include the reference first-type travelplans and the reference second-type travel plans; modifying the trainedlearning machine based on asymmetrical penalties for incorrectlydistinguishing candidate first-type travel plans from candidatesecond-type travel plans, the asymmetrical penalties including unequalfirst and second penalties, the first penalty to be applied forincorrectly classifying a candidate first-type travel plan being greaterthan the second penalty to be applied for incorrectly classifying acandidate second-type travel plan; and providing the modified trainedlearning machine trained to distinguish candidate first-type travelplans from candidate second-type travel plans based on the asymmetricalpenalties for incorrectly distinguishing candidate first-type travelplans from candidate second-type travel plans.
 14. The non-transitorymachine-readable storage medium of claim 13, wherein: the referencemetadata of the reference travel plans indicates sizes of destinationairports, the indicated sizes respectively corresponding to eachreference travel plan among the reference travel plans; and the trainingof the learning machine to distinguish candidate first-type travel plansfrom candidate second-type travel plans is based on the indicated sizesof the destination airports.
 15. The non-transitory machine-readablestorage medium of claim 13, wherein: the reference metadata of thereference travel plans indicates total counts of destination cities, theindicated total counts respectively corresponding to each referencetravel plan among the reference travel plans; and the training of thelearning machine to distinguish candidate first-type travel plans fromcandidate second-type travel plans is based on the indicated totalcounts of destination cities.
 16. The non-transitory machine-readablestorage medium of claim 13, wherein the operations further comprise:accessing a curated database of annual second-type events whose dates ofoccurrence vary by year; and wherein: the reference metadata of thereference travel plans indicates dates of travel respectivelycorresponding to each reference travel plan among the reference travelplans; and the training of the learning machine to distinguish candidatefirst-type travel plans from candidate second-type travel plans is basedon a comparison of the dates of occurrence to the dates of travel.